Vehicle detection and tracking

This juypter notebook contains a pipeline to detect vehicles and tracks them in the image frame.

Init

import numpy as np
import time
import os
import copy
import glob
import random
from collections import deque
import pickle
import cv2
import matplotlib.pyplot as plt
#%matplotlib nbagg
%matplotlib inline

import matplotlib.image as mpimg
from mpl_toolkits.mplot3d import Axes3D
from scipy.ndimage.measurements import label
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import precision_recall_curve, average_precision_score
from sklearn.utils import shuffle
from sklearn.svm import LinearSVC, SVC
from sklearn import linear_model
from skimage.feature import hog
from scipy.ndimage.measurements import label

from IPython.core.display import display, HTML
display(HTML("""<style>
    div#notebook-container    { width: 70%; }
    div#menubar-container     { width: 70%; }
    div#maintoolbar-container { width: 70%; }
</style>"""))

# pretty formatting of np float arrays
float_formatter = lambda x: "%10.5f" % x
np.set_printoptions(formatter={'float_kind' : float_formatter})



#############################################################################
# Constants and variables
#############################################################################

# Cache for training and test features and classifier
path_cache_savefile = "./data_cache.p"

# Small dataset for testing
path_notcar_small = "../non-vehicles_smallset/"
path_car_small    = "../vehicles_smallset/"

# Big dataset for actual usage
path_notcar_big  = "../non-vehicles/"
path_car_big     = "../vehicles/"

path_test_imgs   = "test_images/"
path_output_imgs = "output_images/" 

Helper functions

Most of those functions are adapted from lesson or re-used from previous projects.

# Draw boxes in an image
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=5):
    # Make a copy of the image
    imcopy = np.copy(img)
    # Iterate through the bounding boxes
    for bbox in bboxes:
        # Draw a rectangle given bbox coordinates
        cv2.rectangle(imcopy, tuple(bbox[0]), tuple(bbox[1]), color, thick)

    return imcopy


def convert_color(img, colorspace="YCrCb"):
    if colorspace == "HSV":
        feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
    elif colorspace == "LUV":
        feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
    elif colorspace == "HLS":
        feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    elif colorspace == "YUV":
        feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
    elif colorspace == "YCrCb":
        feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    else:
        print("Colorspace {} is not supported!".format(colorspace))
    return feature_image


# Define a function to compute binned color features  
def bin_spatial(img, size=(32, 32)):
    if len(img.shape) < 3:
        return cv2.resize(img, size).ravel()
    else:
        ch1, ch2, ch3 = [cv2.resize(img[:,:,i], size).ravel() for i in range(img.shape[2])]
        return np.hstack((ch1, ch2, ch3))


# Define a function to compute color histogram features  
def color_hist(img, nbins=32): #, bins_range=(0, 256)):
    # Compute the histogram of the color channels separately
    hist1, hist2, hist3 = [np.histogram(img[:,:,i], bins=nbins) for i in range(3)]
    # Concatenate the histograms into a single feature vector
    hist_features = np.concatenate((hist1[0], hist2[0], hist3[0]))

    return hist_features


# Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):
    # Use skimage.hog() to get both features and a visualization
    if vis:
        features, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell), 
                          cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=False,
                          visualise=vis, feature_vector=feature_vec, block_norm="L1")

        return features, hog_image
        
    else:
        features = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell), 
              cells_per_block=(cell_per_block, cell_per_block), visualise=vis, transform_sqrt=False,
              feature_vector=feature_vec, block_norm="L1")

        return features



    
# Define a function to extract features from a single image window
def single_img_features(img, color_space="RGB", spatial_size=(32, 32), hist_bins=32, orient=9, pix_per_cell=8,
                        cell_per_block=2, hog_channel=0, spatial_feat=True, hist_feat=True, hog_feat=True):    
    
    # Apply color conversion if other than 'RGB'
    if color_space != "RGB":
        feature_image = convert_color(img, color_space)
    else:
        feature_image = np.copy(img)      

    #feature_image = equalize_histogram(feature_image)
    
    # from now on we want work with pixel values ranging from 0 to 1
    feature_image = feature_image.astype(np.float32) / 255.0

    # Compute spatial features if flag is set
    if spatial_feat == True:
        spatial_features = bin_spatial(feature_image, size=spatial_size).reshape(-1)

    # Compute histogram features if flag is set
    if hist_feat == True:
        hist_features = color_hist(feature_image, nbins=hist_bins).reshape(-1)

    # Compute HOG features if flag is set
    if hog_feat == True:
        if hog_channel == -1:
            h1, h2, h3 = [ get_hog_features(feature_image[:,:,ch], orient, pix_per_cell, cell_per_block, vis=False, feature_vec=False)
                                for ch in range(3) ]
            hog_features = np.hstack((h1, h2, h3)).reshape(-1)
        else:
            hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, pix_per_cell,
                                            cell_per_block, vis=False, feature_vec=True)

    return np.hstack((spatial_features, hist_features, hog_features))


# Extract features across a list of images 
def extract_features(imgs, color_space="RGB", spatial_size=(32, 32), hist_bins=32, orient=9, pix_per_cell=8,
                     cell_per_block=2, hog_channel=0, spatial_feat=True, hist_feat=True, hog_feat=True):
    features = []
    for file in imgs:
        image = mpimg.imread(file)
        if file.endswith(".png"):
            image = image * 255.0
            image = image.astype(np.uint8)
            
        feature = single_img_features(image, color_space=color_space, spatial_size=spatial_size, hist_bins=hist_bins, orient=orient,
                                        pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, hog_channel=hog_channel,
                                        spatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat)
        features.append(feature)

    return features


# Define a function you will pass an image and the list of windows to be searched (output of slide_windows())
def search_windows(img, windows, clf, scaler, color_space='RGB', spatial_size=(32, 32), hist_bins=32, 
                    hist_range=(0, 256), orient=9, pix_per_cell=8, cell_per_block=2, 
                    hog_channel=0, spatial_feat=True, hist_feat=True, hog_feat=True):
    on_windows = []
    for window in windows:
        test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))      
        features = single_img_features(test_img, color_space=color_space,spatial_size=spatial_size, hist_bins=hist_bins, 
                            orient=orient, pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, 
                            hog_channel=hog_channel, spatial_feat=spatial_feat, hist_feat=hist_feat, hog_feat=hog_feat)
        test_features = scaler.transform(np.array(features).reshape(1, -1))
        prediction = clf.predict(test_features)
        # If positive (prediction == 1) then save the window
        if prediction == 1:
            on_windows.append(window)

    return on_windows


# Define a single function that can extract features using hog sub-sampling and make predictions
def find_cars(img, colorspace, yrange, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,
              spatial_size, hist_bins, spatial_feat=True, hist_feat=True, cells_per_step=2):
    
    # cells_per_step = 4  # Instead of overlap, define how many cells to step
    image_to_search = img[yrange[0]:yrange[1],:,:]
    
    if colorspace != "RGB":
        ctrans_tosearch = convert_color(image_to_search, colorspace)
    else:
        ctrans_tosearch = np.copy(image_to_search)

    #ctrans_tosearch = equalize_histogram(ctrans_tosearch)
    ctrans_tosearch = ctrans_tosearch.astype(np.float32) / 255
            
    if scale != 1.0:
        imshape = ctrans_tosearch.shape
        ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
        
    ch1, ch2, ch3 = [ ctrans_tosearch[:,:,i] for i in range(3) ]

    # Define blocks and steps as above
    nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
    nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1 
    nfeat_per_block = orient*cell_per_block**2
    
    # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
    window = 64
    nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
    nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
    nysteps = (nyblocks - nblocks_per_window) // cells_per_step
    
    # Compute individual channel HOG features for the entire image
    hog1, hog2, hog3 = [ get_hog_features(ch, orient, pix_per_cell, cell_per_block, feature_vec=False) for ch in [ch1, ch2, ch3] ]
    
    all_bboxes = []
    bboxes = []
    test_features = []
    for xb in range(nxsteps):
        for yb in range(nysteps):
            ypos = yb*cells_per_step
            xpos = xb*cells_per_step
            # Extract HOG for this window
            hw1, hw2, hw3 = [ hog[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() for hog in [hog1, hog2, hog3] ]
            hog_features = np.hstack((hw1, hw2, hw3))
            xleft = xpos*pix_per_cell
            ytop  = ypos*pix_per_cell
            
            # Extract the image patch
            subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (window,window))
          
            # Get color features
            spatial_features = bin_spatial(subimg, size=spatial_size)
            hist_features    = color_hist(subimg, nbins=hist_bins)

            # Scale features and make a prediction
            feat = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
            test_features.append(feat)
            
            xbox_left = np.int(xleft*scale)
            ytop_draw = np.int(ytop*scale)
            win_draw  = np.int(window*scale)
            all_bboxes.append(((xbox_left, ytop_draw+yrange[0]), (xbox_left+win_draw,ytop_draw+win_draw+yrange[0])))

    # get predictions for all patches
    all_bboxes = np.array(all_bboxes)
    test_prediction = svc.predict(np.concatenate(test_features)).astype(np.uint8)
    bboxes = all_bboxes[test_prediction == 1]

    return bboxes, all_bboxes


def add_heat(heatmap, bbox_list):
    # Iterate through list of bboxes
    for box in bbox_list:
        # Add += 1 for all pixels inside each bbox
        # Assuming each "box" takes the form ((x1, y1), (x2, y2))
        heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1

    # Return updated heatmap
    return heatmap


def draw_labeled_bboxes(img, labels, color=(0,0,255), thick=5):
    # Iterate through all detected cars
    for car_number in range(1, labels[1]+1):
        # Find pixels with each car_number label value
        nonzero = (labels[0] == car_number).nonzero()
        # Identify x and y values of those pixels
        nonzeroy = np.array(nonzero[0])
        nonzerox = np.array(nonzero[1])
        # Define a bounding box based on min/max x and y
        bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
        # Draw the box on the image
        img = draw_boxes(img, [bbox], color=color, thick=thick)
        
    return img


def heatmap_filter(image, bboxes, thres=1.0, heat_clip=(0, 255), color=(0,0,255), thick=5):
    heat = np.zeros_like(image[:,:,0]).astype(np.float)
    # Add heat to each box in box list
    heat = add_heat(heat, bboxes)
    # Apply threshold to help remove false positives, zero out pixels below the threshold
    heat[heat <= thres] = 0
    # Visualize the heatmap when displaying    
    heatmap = np.clip(heat, *heat_clip)
    # Find final boxes from heatmap using label function
    labels = label(heatmap)
    draw_img = draw_labeled_bboxes(np.copy(image), labels, color, thick=thick)    
    return draw_img, heatmap


# Define a function to return some characteristics of the dataset 
def data_look(car_list, notcar_list):
    data_dict = {}
    # Define a key in data_dict "n_cars" and store the number of car images
    data_dict["n_cars"] = len(car_list)
    # Define a key "n_notcars" and store the number of notcar images
    data_dict["n_notcars"] = len(notcar_list)
    # Read in a test image, either car or notcar
    # Define a key "image_shape" and store the test image shape 3-tuple
    img = mpimg.imread(car_list[0])
    data_dict["image_shape"] = np.array(img).shape
    # Define a key "data_type" and store the data type of the test image.
    data_dict["data_type"] = np.array(img).dtype
    # Return data_dict
    return data_dict


def equalize_histogram(img, clipLimit=2.0, tileGridSize=(8,8)):
    # use histogram equalization to make sure image has a good contrast
    # for edge detection and thresholding
    clahe = cv2.createCLAHE(clipLimit=clipLimit, tileGridSize=tileGridSize)
    tmp = np.copy(img)
    if len(img.shape) == 3:
        for i in range(img.shape[2]):
            tmp[:,:,i] = clahe.apply(img[:,:,i]) 
    else:
        tmp = clahe.apply(img)
    return tmp

Dataset exploration

The dataset which came with this project is tiny excerpt from the GTI, KITTI and Udacity datasets. It contains around 8.8k car and 8.9k non-cars pictures which are 64x64 pixels in size.

dataset="big" # "small" or "big"

if dataset == "small":
    images_cars    = glob.glob(path_car_small + "**/*.jpeg", recursive=True)
    images_notcars = glob.glob(path_notcar_small + "**/*.jpeg", recursive=True)
else:
    images_cars = glob.glob(path_car_big + "**/*.png", recursive=True)
    images_notcars = glob.glob(path_notcar_big + "**/*.png", recursive=True)

fig, axs = plt.subplots(4,8, figsize=(10, 6))
fig.subplots_adjust(hspace = 0.2, wspace = 0.05)
axs = axs.ravel()

for i in range(16):
    img = mpimg.imread(images_cars[np.random.randint(0, len(images_cars))])
    axs[i].axis("off")
    axs[i].set_title("car", fontsize=10)
    axs[i].imshow(img)
for i in range(16,32):
    img = mpimg.imread(images_notcars[np.random.randint(0, len(images_notcars))])
    axs[i].axis("off")
    axs[i].set_title("noncar", fontsize=10)
    axs[i].imshow(img)

Visualization of spatial features

# Read in the image
image_names = glob.glob(path_test_imgs+"*.jpg")
fig, axs = plt.subplots(2,2, figsize=(14, 10))
bboxes = [ ((400, 400), (450, 450)), ((810, 410), (860, 460)) ]

for i, (bbox, image_name) in enumerate(zip(bboxes, image_names[0:2])):
    image = mpimg.imread(image_name)
    image_box = draw_boxes(image, [bbox], color=(0, 0, 255), thick=6)
    axs[0][i].imshow(image_box)
    axs[0][i].axis("off")
    axs[0][i].set_title(image_name)
    image_small = image[bbox[0][1]:bbox[1][1], bbox[0][0]:bbox[1][0], :]
    image_small = cv2.resize(image_small, (16,16))
    axs[1][i].imshow(image_small)
    axs[1][i].set_title("Binned 16x16 pixel spatial feature")
plt.show()

Visualization of histogram features

# Read in the image
image_names = glob.glob(path_test_imgs+"*.jpg")

image = mpimg.imread(image_names[0])

# Take histograms in R, G, and B
rhist, ghist, bhist = [np.histogram(image[:,:,i], bins=32, range=(0, 256)) for i in range(3)]

# Generating bin centers
bin_edges = rhist[1]
bin_centers = (bin_edges[1:] + bin_edges[0:len(bin_edges)-1]) / 2

fig = plt.figure(figsize=(10,5))
plt.imshow(image)
plt.axis("off")

# Plot a figure with all three bar charts
fig = plt.figure(figsize=(16,3))
plt.subplot(131)
plt.bar(bin_centers, rhist[0])
plt.xlim(0, 256)
plt.title('R Histogram')
plt.subplot(132)
plt.bar(bin_centers, ghist[0])
plt.xlim(0, 256)
plt.title('G Histogram')
plt.subplot(133)
plt.bar(bin_centers, bhist[0])
plt.xlim(0, 256)
plt.title('B Histogram')
plt.show()

Visualizing HOG features on images

fig, ax = plt.subplots(2,4, figsize=(10, 6))
fig.subplots_adjust(hspace = 0.1, wspace = 0.05)

for i in range(2):
    car_ind    = np.random.randint(0, len(images_cars))
    notcar_ind = np.random.randint(0, len(images_notcars))

    # Read in random car/not-car images
    print(images_cars[car_ind])
    print(images_notcars[notcar_ind])
    car_image = mpimg.imread(images_cars[car_ind])
    if images_cars[car_ind].endswith(".png"):
        car_image = car_image * 255.0
        car_image = car_image.astype(np.uint8)
    notcar_image = mpimg.imread(images_notcars[notcar_ind])
    if images_notcars[notcar_ind].endswith(".png"):
        notcar_image = notcar_image * 255.0
        notcar_image = notcar_image.astype(np.uint8)
    #car_image = equalize_histogram(car_image)
    car_gray = cv2.cvtColor(car_image, cv2.COLOR_RGB2GRAY)
    notcar_gray = cv2.cvtColor(notcar_image, cv2.COLOR_RGB2GRAY)
    
    # Call function with vis=True to see an image output
    _, hog_carimage = get_hog_features(car_gray, orient=12, pix_per_cell=8, cell_per_block=8, vis=True, feature_vec=True)
    # Call function with vis=True to see an image output
    _, hog_notcarimage = get_hog_features(notcar_gray, orient=12, pix_per_cell=8, cell_per_block=8, vis=True, feature_vec=True)

    ax[i][0].imshow(car_image)
    ax[i][0].set_title("Car Image", fontsize=10)
    ax[i][0].axis("off")
    ax[i][1].imshow(hog_carimage, cmap="gray")
    ax[i][1].set_title("Car HOG features", fontsize=10)
    ax[i][1].axis("off")
    ax[i][2].imshow(notcar_image)
    ax[i][2].set_title("Non-Car Image", fontsize=10)
    ax[i][2].axis("off")
    ax[i][3].imshow(hog_notcarimage, cmap="gray")
    ax[i][3].set_title("Non-Car HOG features", fontsize=10)
    ax[i][3].axis("off")

../vehicles/KITTI_extracted/5695.png
../non-vehicles/Extras/extra1716.png
../vehicles/GTI_Far/image0704.png
../non-vehicles/GTI/image2049.png

Train SVM classifier on dataset

Dataset selection, exploration and feature extraction

dataset="big" # "small" or "big"

if dataset == "small":
    images_cars    = glob.glob(path_car_small + "**/*.jpeg", recursive=True)
    images_notcars = glob.glob(path_notcar_small + "**/*.jpeg", recursive=True)
else:
    images_cars = glob.glob(path_car_big + "**/*.png", recursive=True)
    images_notcars = glob.glob(path_notcar_big + "**/*.png", recursive=True)


# if we already saved all features
if os.path.isfile(path_cache_savefile):
    # Load the training and test features:
    with open(path_cache_savefile, mode="rb") as f:
        data = pickle.load(f)

    car_features     = data["cars_features"]
    notcar_features  = data["notcars_features"]
    colorspace       = data["colorspace"]
    orient           = data["orient"]
    pix_per_cell     = data["pix_per_cell"]
    cell_per_block   = data["cell_per_block"]
    hog_channel      = data["hog_channel"]
    spatial_size     = data["spatial_size"]
    hist_bins        = data["hist_bins"]
    spatial_feat     = data["spatial_feat"]
    hist_feat        = data["hist_feat"]
    hog_feat         = data["hog_feat"]
    print("Loaded parameters!")
else:
    
    # Reduce the sample size
    sample_size = 5000
    cars = images_cars #[0:sample_size]
    notcars = images_notcars #[0:sample_size]

    data_info = data_look(images_cars, images_notcars)
    print("Dataset contains ", data_info["n_cars"], " cars and", data_info["n_notcars"], " non-cars")
    print("of size: ", data_info["image_shape"], " and data type:", data_info["data_type"])

    ### Tweak these parameters and see how the results change.
    colorspace     = "YCrCb" # possible values: RGB, HSV, LUV, HLS, YUV, YCrCb
    orient         = 9  # HOG orientations
    pix_per_cell   = 8 # HOG pixels per cell
    cell_per_block = 2 # HOG cells per block
    hog_channel    = -1 # Can be 0, 1, 2, or -1 for all channels
    spatial_size   = (32, 32) # Spatial binning dimensions
    hist_bins      = 32    # Number of histogram bins
    spatial_feat   = True # Spatial features on or off
    hist_feat      = True # Histogram features on or off
    hog_feat       = True # HOG features on or off

    t=time.time()
    car_features    = extract_features(cars, color_space=colorspace, spatial_size=spatial_size, hist_bins=hist_bins, 
                                       orient=orient, pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, hog_channel=hog_channel)
    notcar_features = extract_features(notcars, color_space=colorspace, spatial_size=spatial_size, hist_bins=hist_bins,
                                       orient=orient, pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, hog_channel=hog_channel)
    t2 = time.time()
    print("It took {:0.2f} seconds to extract features...".format(t2-t))
    
    # Save the training and test features:
    data = {
        "cars_features"    : car_features,
        "notcars_features" : notcar_features,
        "colorspace"       : colorspace,
        "orient"           : orient,
        "pix_per_cell"     : pix_per_cell,
        "cell_per_block"   : cell_per_block,
        "hog_channel"      : hog_channel,
        "spatial_size"     : spatial_size,
        "hist_bins"        : hist_bins,
        "spatial_feat"     : spatial_feat,
        "hist_feat"        : hist_feat,
        "hog_feat"         : hog_feat
    }
    with open(path_cache_savefile, mode="wb") as f:
        pickle.dump(data, f)

# Create an array stack of feature vectors
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)

# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))

# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
scaled_X, y = shuffle(scaled_X, y, random_state=rand_state)
rand_state = np.random.randint(0, 100)
scaled_X, y = shuffle(scaled_X, y, random_state=rand_state)
rand_state = np.random.randint(0, 100)
scaled_X, y = shuffle(scaled_X, y, random_state=rand_state)
X_train, X_test, y_train, y_test = train_test_split(scaled_X, y, test_size=0.2, random_state=rand_state)
print("Colorspace: ", colorspace, "spatial_size: ", spatial_size , "hist_bins: ", hist_bins)
print('Using:', orient, 'orientations', pix_per_cell, 'pixels per cell and', cell_per_block, 'cells per block')
print('Feature vector length:', len(X_train[0]))

Loaded parameters!
Colorspace:  YCrCb spatial_size:  (32, 32) hist_bins:  32
Using: 9 orientations 8 pixels per cell and 2 cells per block
Feature vector length: 8460

Training classifier and testing accuracy on test set

if os.path.isfile(path_cache_savefile):
    # Load the training and test features:
    with open(path_cache_savefile, mode="rb") as f:
        data = pickle.load(f)

    if "svc" in data:
        svc = data["svc"]
        print("Loaded linear SVC classifier:")
    else:
        svc = None

if not svc:
    print("Training linear SVC classifier from scratch")
    # Use a linear SVC 
    svc = LinearSVC(C=1.0)
    # Check the training time for the SVC
    t=time.time()
    svc.fit(X_train, y_train)
    t2 = time.time()
    print("It took {:0.2f} seconds to train SVC...".format(t2-t))
    
    data["svc"] = svc
    with open(path_cache_savefile, mode="wb") as f:
        pickle.dump(data, f)

print(svc)

Loaded linear SVC classifier:
LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
     intercept_scaling=1, loss='squared_hinge', max_iter=1000,
     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
     verbose=0)

# comparing to other classifier
coef = np.array([[ -6.85043858e-03, -7.12555239e-03, -3.01890009e-03, -1.17397343e-03, -8.90345542e-04, -7.46935320e-04, 2.14676265e-04, 7.33196871e-04, 1.48111643e-04, -2.50950565e-03, 3.36022813e-04, -1.89328062e-03, -2.98447356e-03, -1.56333341e-03, -4.81315603e-04, -2.56254599e-04, 1.16763562e-03, 6.46393114e-04, -4.06448112e-03, -1.68068879e-03, -2.47167238e-03, -3.86570590e-04, -4.67173242e-04, 1.97290661e-03, 3.69488887e-03, 4.52333404e-03, 6.19077284e-03, 4.81735129e-03, 3.54855279e-03, 4.34105806e-03, 8.59359104e-03, 7.21568847e-03, -8.79695541e-03, -3.46153634e-03, -4.28928168e-04, 2.12224489e-03, 2.90043521e-04, 2.26900298e-03, 2.23633708e-03, 1.03876570e-03, 2.18907265e-03, -6.61625270e-04, -3.07572435e-04, -5.07569190e-03, -3.65408732e-03, 2.95255636e-03, 2.44143888e-03, 6.06579814e-04, 1.23141232e-03, 2.26422969e-03, -7.58296497e-04, -1.98522068e-03, 1.04252501e-03, 3.02356321e-03, 6.37297469e-03, 4.62647801e-03, 3.75347702e-04, -2.32962599e-03, -9.33259125e-04, 3.77637349e-04, -2.05267606e-03, 5.38517659e-04, 3.65852278e-03, 3.40699135e-03, -4.44649179e-03, -2.29166160e-04, 1.72627040e-03, -1.40108837e-03, -2.69960467e-03, -1.09223223e-03, -6.94561845e-04, -2.89224544e-03, -2.12356317e-03, -2.75392464e-03, -2.03658189e-03, -6.24054275e-03, -2.25681417e-03, 2.47737938e-03, 1.35702282e-03, -1.01414055e-03, -1.80454489e-03, -3.52393797e-03, -7.39187364e-03, -6.87010277e-03, -4.69174618e-03, -2.39987661e-03, 3.18342606e-05, -2.38721507e-03, -3.76021743e-03, -3.78931231e-03, -2.15373809e-04, 3.05104729e-03, 5.55358584e-04, 2.57750605e-03, 1.16422092e-03, -1.64903421e-03, -6.90097570e-03, -5.40371647e-03, -1.62287285e-03, -3.35408829e-03, 3.46081038e-04, 1.59958793e-03, 2.65824318e-04, -4.55317242e-03, -4.38701568e-03, -3.59601689e-03, -3.14435593e-03, -2.58359808e-03, -1.23130152e-04, 8.48862910e-04, 2.55258944e-03, 1.61248787e-03, -2.98081451e-03, -1.92298943e-03, -2.14901529e-03, -4.88530253e-03, -4.10777539e-03, -2.64594000e-03, 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2.32390451e-03, 1.13436123e-03, -1.33275588e-03, 2.77920639e-03, 5.12381906e-03, 9.09089724e-04, 2.27199309e-04, -3.02279967e-03, -1.30277032e-04, -2.89489041e-04, -3.67446822e-04, -2.77497992e-04, 1.16552369e-03, 9.92921457e-04, 3.84178209e-04, -1.84185915e-03, -1.22707037e-03, 1.01132105e-03, -2.39800005e-04, 1.35772461e-04, -8.28547971e-04, -2.17942018e-03, 1.66778842e-03, 3.47861755e-03, 2.53122656e-03, 5.33482823e-03, 7.13927173e-03, 7.58942534e-03, 4.52940071e-03, 4.13828264e-03, 6.63688095e-03, 8.91576025e-03, 1.02025710e-03, 4.08289762e-04, -2.34978726e-03, 4.70567460e-04, 3.90731358e-03, 2.37817128e-04, -2.11945533e-03, -3.97128361e-03, -1.85703095e-03, 5.25336659e-04, -5.78990114e-05, -1.64894943e-03, -1.12143614e-03, -3.34892261e-03, -3.35102154e-03, -5.72966120e-03, -6.98185534e-03, -7.38368412e-04, 1.26651673e-03, -1.38477813e-03, -4.40067165e-03, -5.65425395e-04, -3.39674090e-04, -1.67265299e-03, -1.32096046e-03, 9.30472773e-04, -1.55559522e-03, 1.34629811e-04, 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2.04246767e-04, -7.24683377e-04, 2.31198691e-03, 1.27449227e-03, 4.67903935e-03, 5.86466238e-03, 6.77672014e-03, 2.03076187e-03, 3.47398300e-03, 1.59013238e-03, 1.65395171e-04, -1.16009317e-03, -3.64153380e-03, -2.62921330e-03, -1.42555762e-04, -9.50741727e-04, -4.43214195e-03, -4.59143901e-03, -1.91297312e-03, -2.86026800e-03, -1.22281469e-03, -6.27517446e-04, -2.94057338e-03, -4.89603495e-03, -7.71877248e-04, 3.34023165e-03, 1.28763797e-03, -9.88950775e-04, 3.32742204e-03, 5.20493584e-03, 4.71999459e-03, 3.36593367e-03, 4.31049027e-03, -2.32356512e-04, 2.09092908e-03, -1.15107321e-03, 4.17566749e-03, 7.07046085e-03, 3.94332601e-03, 3.99828421e-04, -3.02920871e-03, -3.14202547e-03, -6.62651058e-03, -4.93388402e-03, -5.53997711e-03, -5.68087959e-03, -2.27431676e-03, -3.01722692e-03, -1.49505037e-03, 1.11119878e-03, 2.64345781e-03, 1.22694576e-03, -1.88019317e-03, -6.89826026e-03, 2.66665386e-03, 3.91291299e-03, 1.95431174e-03, -9.74486443e-04, -1.92672576e-03, -1.56916605e-03, 5.65328745e-03, 4.44698818e-03, 4.26657559e-03, 2.92005670e-03, 1.74055852e-03, 8.90741140e-04, 4.46972898e-03, 4.59607964e-03, 3.05054804e-03, 6.36896358e-03, 4.60980530e-03, 3.24808242e-03, -1.51431004e-03, -3.97882582e-03, -3.86966593e-03, -2.60002302e-03, -1.71732985e-03, -2.18850157e-03, -8.60519488e-04, 1.45837834e-03, 5.11988449e-03, 6.28812632e-03, 3.95551214e-03, 4.62297600e-03, -1.74410876e-03, -5.55958481e-03, 8.42178659e-03, 1.10506347e-02, 6.02732505e-03, 3.64752032e-03, 1.40683722e-03, 2.25170535e-04, 3.92558633e-03, 3.95904052e-03, 2.11195456e-06, 3.43452891e-04, -1.44840108e-03, -5.55273891e-05, 2.64540287e-03, 2.20468191e-03, 4.40843450e-03, 7.96665771e-03, 5.44976849e-03, 4.47862552e-03, 1.87438244e-03, -2.74306029e-04, -7.03091366e-04, -6.03958824e-04, -2.56942881e-04, 4.12072741e-04, 1.99342431e-03, 4.18923527e-03, 6.15138065e-03, 6.84177018e-03, -1.55376933e-04, -2.94286728e-03, -4.41869955e-03, -2.00611919e-03, 1.13549254e-02, 9.42333245e-03, 3.27643998e-03, 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-2.30221692e-04, -1.05872740e-03, 4.91070620e-03, 4.10231230e-03, 7.60893264e-03, 6.74166402e-03, -4.55770722e-03, -5.75143279e-04, 1.50002859e-03, -8.22389267e-04, 8.49488202e-03, -2.18650866e-03, 6.84769009e-03, 2.71362282e-03, 2.31031678e-03, -5.88423420e-03, 7.07321226e-03, -2.39893603e-03, -3.10267687e-03, 1.04763784e-03, 2.17063227e-03, 4.42638508e-03, 1.97045209e-03, -2.48127240e-03, -7.84438418e-03, 7.66364336e-03, 3.18237084e-04, 7.25123615e-03, 1.46673692e-03]])
svc2 = copy.deepcopy(svc)
svc2.coef_ = coef
svc2.intercept_ = [-0.00970871]
fig = plt.figure(figsize=(15,10))
plt.plot(a.ravel())
plt.plot(svc2.coef_.ravel())
plt.legend(["previous model", "mine"])
plt.show()

"""
parameters = {'class_weight':(None, 'balanced'), 'C':[0.1, 1.0, 10, 100]}
svc = LinearSVC()
clf = GridSearchCV(svc, parameters, n_jobs=3)
clf.fit(X_train, y_train)
sorted(clf.cv_results_.keys())
"""

"\nparameters = {'class_weight':(None, 'balanced'), 'C':[0.1, 1.0, 10, 100]}\nsvc = LinearSVC()\nclf = GridSearchCV(svc, parameters, n_jobs=3)\nclf.fit(X_train, y_train)\nsorted(clf.cv_results_.keys())\n"

"""
print(clf.cv_results_["split1_test_score"])
"""

'\nprint(clf.cv_results_["split1_test_score"])\n'

Brief test with deep learning classifier from keras

from keras.applications import InceptionV3
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Input, AveragePooling2D, Convolution2D, Dense, Dropout, Flatten, Input, MaxPooling2D, ZeroPadding2D
from keras.models import Model, Sequential

batch_size = 256
nb_epoch   = 10

# TODO: maybe InceptionV3 is a little bit heavy, I should try a smaller network...
"""
datagen = ImageDataGenerator(
        featurewise_center=False,  # set input mean to 0 over the dataset
        samplewise_center=False,  # set each sample mean to 0
        featurewise_std_normalization=False,  # divide inputs by std of the dataset
        samplewise_std_normalization=False,  # divide each input by its std
        zca_whitening=False,  # apply ZCA whitening
        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
        horizontal_flip=True,  # randomly flip images
        vertical_flip=False)  # randomly flip images

# Compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied).
#datagen.fit(X_train_upscaled)

# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)
# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
    layer.trainable = False

# add a global spatial average pooling layer
x = base_model.output
x = Dropout(0.5)(x)
predictions = Dense(2, activation='softmax')(x)

# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)
model.summary()

# compile the model (should be done *after* setting layers to non-trainable)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')

# Fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(X_train_upscaled, y_train, batch_size=batch_size),
                    steps_per_epoch=X_train.shape[0] // batch_size,
                    validation_data=(X_test_upscaled, y_test),
                    epochs=nb_epoch, verbose=1)
"""

"\ndatagen = ImageDataGenerator(\n        featurewise_center=False,  # set input mean to 0 over the dataset\n        samplewise_center=False,  # set each sample mean to 0\n        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n        samplewise_std_normalization=False,  # divide each input by its std\n        zca_whitening=False,  # apply ZCA whitening\n        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\n        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n        horizontal_flip=True,  # randomly flip images\n        vertical_flip=False)  # randomly flip images\n\n# Compute quantities required for featurewise normalization\n# (std, mean, and principal components if ZCA whitening is applied).\n#datagen.fit(X_train_upscaled)\n\n# create the base pre-trained model\nbase_model = InceptionV3(weights='imagenet', include_top=False)\n# first: train only the top layers (which were randomly initialized)\n# i.e. freeze all convolutional InceptionV3 layers\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# add a global spatial average pooling layer\nx = base_model.output\nx = Dropout(0.5)(x)\npredictions = Dense(2, activation='softmax')(x)\n\n# this is the model we will train\nmodel = Model(inputs=base_model.input, outputs=predictions)\nmodel.summary()\n\n# compile the model (should be done *after* setting layers to non-trainable)\nmodel.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n\n# Fit the model on the batches generated by datagen.flow().\nmodel.fit_generator(datagen.flow(X_train_upscaled, y_train, batch_size=batch_size),\n                    steps_per_epoch=X_train.shape[0] // batch_size,\n                    validation_data=(X_test_upscaled, y_test),\n                    epochs=nb_epoch, verbose=1)\n"

"""
clf = linear_model.SGDClassifier(max_iter=600, n_jobs=-1, loss="hinge")
t=time.time()
clf.fit(X_train, y_train)
t2 = time.time()
print("It took {:0.2f} seconds to train SGDClassifier...".format(t2-t))
"""

'\nclf = linear_model.SGDClassifier(max_iter=600, n_jobs=-1, loss="hinge")\nt=time.time()\nclf.fit(X_train, y_train)\nt2 = time.time()\nprint("It took {:0.2f} seconds to train SGDClassifier...".format(t2-t))\n'

"""
from sklearn.ensemble import BaggingClassifier
from sklearn.multiclass import OneVsRestClassifier
n_estimators = 5
clf2 = OneVsRestClassifier(BaggingClassifier(LinearSVC(C=1.0, class_weight="balanced"), n_jobs=-1,
                                             max_samples=1.0 / n_estimators, n_estimators=n_estimators))
t=time.time()
clf2.fit(X_train, y_train)
t2 = time.time()
print("It took {:0.2f} seconds to train BaggingClassifier...".format(t2-t))
"""

'\nfrom sklearn.ensemble import BaggingClassifier\nfrom sklearn.multiclass import OneVsRestClassifier\nn_estimators = 5\nclf2 = OneVsRestClassifier(BaggingClassifier(LinearSVC(C=1.0, class_weight="balanced"), n_jobs=-1,\n                                             max_samples=1.0 / n_estimators, n_estimators=n_estimators))\nt=time.time()\nclf2.fit(X_train, y_train)\nt2 = time.time()\nprint("It took {:0.2f} seconds to train BaggingClassifier...".format(t2-t))\n'

# Check the score of the SVC
score = svc.score(X_test, y_test)
print("Test Accuracy of SVC = {:0.3f}".format(score))

# Check the score of the BaggingClassifier
#score = clf2.score(X_test, y_test)
#print("Test Accuracy of clf2 = {:0.3f}".format(score))

# Check the score of the SGDClassifier
#score = clf.score(X_test, y_test)
#print("Test Accuracy of SGDClassifier = {:0.3f}".format(score))

Test Accuracy of SVC = 0.999

y_score = svc.predict(X_test)
average_precision    = average_precision_score(y_test, y_score)
precision, recall, _ = precision_recall_curve(y_test, y_score)

plt.step(recall, precision, color='b', alpha=0.2, where='post')
plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title("2-class Precision-Recall curve: AP={0:0.2f}".format(average_precision))
plt.show()

car_indexes     = random.sample(range(len(images_cars)), 5)
notcar_indexes  = random.sample(range(len(images_notcars)), 5)

for idx in car_indexes:
    car_image = mpimg.imread(images_cars[idx])
    fig = plt.figure(figsize=(3,3))
    plt.imshow(car_image)
    plt.show()
    print(svc.predict(car_features[idx].reshape(1,-1)), clf.predict(car_features[idx].reshape(1,-1)),
          clf2.predict(car_features[idx].reshape(1,-1)))

for idx in notcar_indexes:
    notcar_image = mpimg.imread(images_notcars[idx])
    fig = plt.figure(figsize=(3,3))
    plt.imshow(notcar_image)
    plt.show()
    print(svc.predict(notcar_features[idx].reshape(1,-1)), clf.predict(notcar_features[idx].reshape(1,-1)),
          clf2.predict(notcar_features[idx].reshape(1,-1)))

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-131-1409181aaa6c> in <module>()
      7     plt.imshow(car_image)
      8     plt.show()
----> 9     print(svc.predict(car_features[idx].reshape(1,-1)), clf.predict(car_features[idx].reshape(1,-1)),
     10           clf2.predict(car_features[idx].reshape(1,-1)))
     11 

NameError: name 'clf' is not defined

Testing on example images by drawing bounding boxes around vehicles

# Min and max in y to search in slide_window()
y_start_stop = [[400, 580], [400, 650], [400, 720]]
scales       =  [1.2      , 1.6       , 2.0      ]
colors       =  [(255,0,0), (0,255,0) , (0,0,255)]
cells_per_steps = [3 , 3 , 4]

image_names = glob.glob(path_test_imgs+"*.jpg")

image = mpimg.imread(image_names[0])
if image_name.endswith(".png"):
    image = image * 255.0
    image = image.astype(np.uint8)

out_img_all = np.copy(image)
for i, (cells_per_step, color, scale, y_range) in enumerate(zip(cells_per_steps, colors, scales, y_start_stop)):
    bboxes, all_bboxes = find_cars(out_img_all, colorspace, y_range, scale, svc2, X_scaler, orient, pix_per_cell, cell_per_block,
                                   spatial_size, hist_bins, spatial_feat=True, hist_feat=True, cells_per_step=cells_per_step)
    out_img_all = draw_boxes(bboxes=all_bboxes, img=out_img_all, color=color, thick=3)    

fig = plt.figure(figsize=(12,10))
plt.title("All windows")
plt.imshow(out_img_all)
plt.show()    

[400, 580]
[400, 650]
[400, 720]

for image_name in image_names:
    image = mpimg.imread(image_name)
    if image_name.endswith(".png"):
        image = image * 255.0
        image = image.astype(np.uint8)

    out_img = np.copy(image)
    out_img_all = np.copy(image)
    bboxes_list = []
    time1 = time.time()
    for i, (cells_per_step, color, scale, y_range) in enumerate(zip(cells_per_steps, colors, scales, y_start_stop)):
        bboxes, all_bboxes = find_cars(out_img, colorspace, y_range, scale, svc2, X_scaler, orient, pix_per_cell, cell_per_block,
                                       spatial_size, hist_bins, spatial_feat=True, hist_feat=True, cells_per_step=cells_per_step)
        print("scale %.1f: %d boxes total (%d detections)" % (scale, len(all_bboxes), len(bboxes)))
        bboxes_list.extend(bboxes)
        out_img = draw_boxes(bboxes=bboxes, img=out_img, color=color, thick=2)    
        out_img_all = draw_boxes(bboxes=all_bboxes, img=out_img_all, color=color, thick=3)    

    out_img, _ = heatmap_filter(out_img, bboxes_list, thres=3.5, heat_clip=(0, 255), color=(238,130,238), thick=8)
    time2 = time.time()
    print("It took {:.2f}s to process picture".format(time2-time1))
    fig, axs = plt.subplots(1, 2, figsize=(18,14))
    fig.subplots_adjust(hspace = .004, wspace=.008)
    axs[0].set_title("Found windows")
    axs[0].imshow(out_img)
    axs[0].axis("off")
    axs[1].set_title("All windows")
    axs[1].imshow(out_img_all)
    axs[1].axis("off")
    plt.show()

scale 1.2: 123 boxes total (1 detections)
scale 1.6: 90 boxes total (0 detections)
scale 2.0: 54 boxes total (0 detections)
It took 0.61s to process picture

scale 1.2: 123 boxes total (22 detections)
scale 1.6: 90 boxes total (8 detections)
scale 2.0: 54 boxes total (3 detections)
It took 0.65s to process picture

scale 1.2: 123 boxes total (22 detections)
scale 1.6: 90 boxes total (17 detections)
scale 2.0: 54 boxes total (6 detections)
It took 0.64s to process picture

scale 1.2: 123 boxes total (2 detections)
scale 1.6: 90 boxes total (1 detections)
scale 2.0: 54 boxes total (1 detections)
It took 0.65s to process picture

scale 1.2: 123 boxes total (18 detections)
scale 1.6: 90 boxes total (8 detections)
scale 2.0: 54 boxes total (8 detections)
It took 0.65s to process picture

scale 1.2: 123 boxes total (11 detections)
scale 1.6: 90 boxes total (5 detections)
scale 2.0: 54 boxes total (4 detections)
It took 0.69s to process picture

Showing heat map

for image_name in image_names:
    image = mpimg.imread(image_name)
    if image_name.endswith(".png"):
        image = image * 255.0
        image = image.astype(np.uint8)

    out_img = np.copy(image)
    out_img_all = np.copy(image)
    bboxes_list = []
    time1 = time.time()
    for i, (cells_per_step, color, scale, y_range) in enumerate(zip(cells_per_steps, colors, scales, y_start_stop)):
        bboxes, all_bboxes = find_cars(out_img, colorspace, y_range, scale, svc2, X_scaler, orient, pix_per_cell, cell_per_block,
                                       spatial_size, hist_bins, spatial_feat=True, hist_feat=True, cells_per_step=cells_per_step)
        print("scale %.1f: %d boxes total (%d detections)" % (scale, len(all_bboxes), len(bboxes)))
        bboxes_list.extend(bboxes)
        out_img = draw_boxes(bboxes=bboxes, img=out_img, color=color, thick=2)    
        out_img_all = draw_boxes(bboxes=all_bboxes, img=out_img_all, color=color, thick=3)    

    out_img, heatmap = heatmap_filter(out_img, bboxes_list, thres=3.5, heat_clip=(0, 255), color=(238,130,238), thick=8)
    time2 = time.time()
    print("It took {:.2f}s to process picture".format(time2-time1))
    fig, axs = plt.subplots(1, 2, figsize=(18,14))
    fig.subplots_adjust(hspace = .004, wspace=.008)
    axs[0].set_title("Found windows")
    axs[0].imshow(out_img)
    axs[0].axis("off")
    axs[1].set_title("Heat map")
    axs[1].imshow(heatmap, cmap='hot')
    axs[1].axis("off")
    plt.show()

scale 1.2: 123 boxes total (1 detections)
scale 1.6: 90 boxes total (0 detections)
scale 2.0: 54 boxes total (0 detections)
It took 0.67s to process picture

scale 1.2: 123 boxes total (22 detections)
scale 1.6: 90 boxes total (8 detections)
scale 2.0: 54 boxes total (3 detections)
It took 0.66s to process picture

scale 1.2: 123 boxes total (22 detections)
scale 1.6: 90 boxes total (17 detections)
scale 2.0: 54 boxes total (6 detections)
It took 0.72s to process picture

scale 1.2: 123 boxes total (2 detections)
scale 1.6: 90 boxes total (1 detections)
scale 2.0: 54 boxes total (1 detections)
It took 0.64s to process picture

scale 1.2: 123 boxes total (18 detections)
scale 1.6: 90 boxes total (8 detections)
scale 2.0: 54 boxes total (8 detections)
It took 0.64s to process picture

scale 1.2: 123 boxes total (11 detections)
scale 1.6: 90 boxes total (5 detections)
scale 2.0: 54 boxes total (4 detections)
It took 0.68s to process picture

Running vehicle detection on videos

# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
from moviepy.editor import *

# save detected windows across image frames in a queue which resembles a ringbuffer 
nr_frames = 20
boxes_queue = deque(maxlen=len(scales)*nr_frames)

video_output_folder = "videos_output/"
videos = [
   "project_video.mp4",
   #"test_video.mp4"
]


def process_image(image):
    xsize, ysize = img_size = (image.shape[1], image.shape[0])
    out_img = np.copy(image)
    bboxes_list = []

    for i, (cells_per_step, color, scale, y_range) in enumerate(zip(cells_per_steps, colors, scales, y_start_stop)):
        bboxes, all_bboxes = find_cars(out_img, colorspace, y_range, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block,
                                       spatial_size, hist_bins, spatial_feat=True, hist_feat=True, cells_per_step=cells_per_step)
        boxes_queue.extend(bboxes)

    out_img, heatmap = heatmap_filter(out_img, boxes_queue, thres=nr_frames/2, heat_clip=(0, 255), color=(255,0,0))
    return out_img


for video in videos:
    if not os.path.exists(video_output_folder):
        os.makedirs(video_output_folder)
    result_path = video_output_folder + video
    if not os.path.isfile(video):
        print("Video %s doesn't exist!")
    else:
        clip1 = VideoFileClip(video) #.subclip(10,20)
        white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
        %time white_clip.write_videofile(result_path, audio=False)

[MoviePy] >>>> Building video videos_output/project_video.mp4
[MoviePy] Writing video videos_output/project_video.mp4

100%|█████████▉| 1260/1261 [14:11<00:00,  1.50it/s]

[MoviePy] Done.
[MoviePy] >>>> Video ready: videos_output/project_video.mp4 

CPU times: user 32min 43s, sys: 58min 39s, total: 1h 31min 23s
Wall time: 14min 11s

Saving videos as animated gifs

videos = [
   "project_video.mp4",
   #"test_video.mp4"
]

video_fps = 10
clip_part = (30.0, 35.0)
resize_factor = 0.5

if not os.path.exists(video_output_folder):
    print("Output folder does not exist?!?")
for video in videos:
    result_path = video_output_folder + video
    if not os.path.isfile(result_path):
        print("Video %s doesn't exist!" % result_path)
        continue
    else:
        clip1 = VideoFileClip(result_path).subclip(*clip_part).resize(resize_factor)
        video_slowdown_factor = video_fps / clip1.fps
        clip1 = clip1.fx(vfx.speedx, video_slowdown_factor)
        %time clip1.write_gif(result_path+".gif", fps=video_fps)

[MoviePy] Building file videos_output/project_video.mp4.gif with imageio

 99%|█████████▉| 125/126 [00:09<00:00, 13.82it/s]

CPU times: user 14.9 s, sys: 192 ms, total: 15.1 s
Wall time: 9.05 s